preselection: preselection
In willja16/GWAS.BAYES: GWAS for Selfing Species

Description Usage Arguments Value Examples

View source: R/Preselection_Function_New.R

Finds significant SNP's

1	preselection(Y,SNPs,number_cores,principal_components,frequentist,controlrate,threshold,nullprob,alterprob,kinship = FALSE,info = FALSE)

`Y`	The reduced matrix of response values
`SNPs`	The reduced SNP matrix where the columns are either 1's or 0's.
`number_cores`	The number of cores on which you would like to parallize this procedure
`principal_components`	The reduced matrix of the principal components.
`frequentist`	A logical value to see whether one would like to use a frequentist multiple comparison test or Bayesian False Discovery based on BIC's. The value of this affects whether values of the next parameters are needed.
`controlrate`	Only used when frequentist = TRUE. This is for which multiple comparison method you would like to use. Examples are "bonferroni" and "BH". See p.adjust for a full list of methods.
`threshold`	The value at which type 1 error rate is held at. .05 in most common literature. Used when frequentist is TRUE or FALSE
`nullprob`	Used when frequentist = FALSE, the probability that is assigned to the null hypothesis.
`alterprob`	Used when frequentist = FALSE, the probability that is assigned to the alternate hypothesis.
`kinship`	The kinship matrix if a model with a kinship component is desired. If not set kinship = FALSE.
`info`	An information matrix where the first row is the chromosome information and the second row in the position information for the SNP's.

`Frequentist Matrix`	The matrix of results when Frequentist = TRUE. The results are formatted as a data.frame with the column Significant being 1 or 0 depending on if the SNP was significant (1 for significant). The P_values column will be the p-values that were calculated for each SNP.
`Bayesian Matrix`	The matrix of results when Frequentist = FALSE. The results are formatted as a data.frame with the column Significant being 1 or 0 depending on if the SNP was significant (1 for significant). The ApprPosteriorProbs column will be the Approximate Posterior Probabilities that were calculated for each SNP.

data("vignette_lm_dat")
Y <- vignette_lm_dat$Phenotype
SNPs <- vignette_lm_dat[,-1]
fullPreprocess <- preprocess_SNPs(SNPs = SNPs,Y = Y,MAF = 0.01,number_cores = 1)
SNPs <- fullPreprocess$SNPs
Y <- fullPreprocess$Y
fullPreprocess$SNPs_Dropped
principal_comp <- pca_function(SNPs = SNPs,number_components = 1,plot_it = FALSE)

preselection(Y = Y, SNPs = SNPs,number_cores = 1, principal_components = principal_comp,frequentist = TRUE,controlrate = "bonferroni",threshold = .05,kinship = FALSE,info = FALSE)